PlotS presents users with a range of eight graph types
to select from:
Certain graph like density, frequency and histogram require only
X-axis. The variable for Y-axis for the other remaining graph has to be
numeric variable. Users can interactively change the variable for the
axes.
The aesthetic choice serves as a valuable function that links a variable to a visual element like color, shape, or line type (dash, dotted, solid). This enables users to add additional variables or differentiate between variables on the graph through aesthetic mapping. This functionality equips PlotS to effectively manage a wide range of data variables for analysis, setting it apart from other visualization tools.
To illustrate, we will use a hypothetical gene expression dataset (refer to Table 1) representing two rice cultivars (IR64 and N22) exposed to two types of treatments (t1 and t2), along with a control (c). Each condition has two replicates (R1, R2). Let’s create a scatter plot with aesthetic color mapped to treatment and Shape to replicate of the data. The resulting graphical representation is depicted in Figure 1.
Table 1. Expression data with two replicates of two rice cultivars under different treatment conditions. | |||
|---|---|---|---|
cultivar | treatment | replicate | fpkm |
IR64 | t1 | R1 | 20.90 |
IR64 | t1 | R2 | 17.75 |
IR64 | t2 | R1 | 5.90 |
IR64 | t2 | R2 | 3.39 |
IR64 | c | R1 | 7.60 |
IR64 | c | R2 | 6.60 |
N22 | t1 | R1 | 10.37 |
N22 | t1 | R2 | 11.93 |
N22 | t2 | R1 | 41.51 |
N22 | t2 | R2 | 33.64 |
N22 | c | R1 | 23.81 |
N22 | c | R2 | 28.01 |
Figure 1. Scatter plot with the chosen aesthetic elements - color and shape
PlotS offers various features for multivariate analysis in addition to the features provided under Aesthetic options. Visualization of the relationship of multiple variables in a data can be done in four ways: 1. Faceting 2. Secondary Y-axis 2. Side graph 3. Inset graph
Faceting creates tables of graphics by splitting the data into subsets and displaying the same graph for each subset. It can either be in grid or wrap. We will use the below Table2 for illustration of faceting. The data are in similar format as in Table 1, but more detail number of rows.
Faceting involves generating sets of visual representations by partitioning data into smaller groups and showcasing identical graphs for each subgroup. This can be achieved using either a wrap or grid arrangement. To exemplify the concept of faceting, we will utilize the provided Table 2. Although the data follows a format akin to Table 1, it contains a more comprehensive range of rows, providing a more detailed perspective.
Figure 2. displaying the wrap faceting
Figure 2. displaying the grid faceting